RESEARCH ARTICLE Open Access

Knowledge about infections is associatedwith antibiotic use: cross-sectional evidencefrom the health survey Northern IrelandJ. Shebehe1* , E. Ottertun2, K. Carlén2 and D. Gustafson2,3

Abstract

Background: Antibiotic overuse is the main modifiable driver of antibiotic resistance. Factors associated withoveruse have been inconsistently reported and vary across populations. Given the burgeoning occurrence ofinfectious diseases around the world, there remains a great need to identify barriers and solutions to the control ofinfections. We examined whether knowledge about infections and antibiotic resistance is associated with antibioticuse in a northern European population sample.

Methods: The Health Survey Northern Ireland 2014/15 was completed by a cross-sectional sample of 4135participants aged > 16 years. Participants were asked whether they had taken an antibiotic in the past 12 months;and six questions were asked concerning knowledge about infections and antibiotic resistance. Correct answers tothe six knowledge questions defined a knowledge score (score range 0–6 correct answers). We used multivariablelogistic regression to estimate odds of self-reported antibiotic use during the last 12 months in association withknowledge score (lowest score, 0/6, as referent), and response to each knowledge question. Covariates includedsex, age group, smoking, alcohol drinking, deprivation index, self-rated health, and satisfaction with life. Results wereoutputted as Odds Ratios (OR) and 95% Confidence Intervals (CI).

Results: Antibiotic use in the past 12 months was reported by 39.0% (1614/4135); and 84.2% (3482/4135) scored <6/6 correct on knowledge statements. Compared to the lowest knowledge score (0/6 correct), the highestknowledge score (6/6 correct) was associated with higher odds of antibiotic use (adjusted OR 2.03, 95% CI [1.46,2.81], p < 0.001), with a P-value < 0.001 for trend with increasing knowledge score. Female sex, age, highdeprivation, and poor general health, were independently associated with higher odds of antibiotic use. Stratifiedanalyses showed sex and age group differences.

Conclusion: Knowledge, and other modifiable and non-modifiable risk factors, were positively associated withantibiotic use in the past 12 months. While the causal direction of these associations could not be determined,given the high prevalence of lesser knowledge, as well as independent contributions of other factors includingsocioeconomic characteristics, health literacy campaigns to raise awareness of antibiotic resistance should take amulti-pronged approach.

Keywords: Health knowledge, attitudes, practice, Health literacy, Antibiotic resistance, Health education, Antibiotics

© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you giveappropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtainpermission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.

* Correspondence: Jacques.Shebehe@oru.se1Clinical Epidemiology and Biostatistics, School of Medical Sciences, ÖrebroUniversity, Örebro, SwedenFull list of author information is available at the end of the article

Shebehe et al. BMC Public Health (2021) 21:1041 https://doi.org/10.1186/s12889-021-11018-x

BackgroundAntimicrobial resistance (AMR) is a global crisis withserious consequences for the individual, public health,and the economy [1]. It has been estimated that during2015, sixteen antibiotic resistant bacteria caused > 33,000 deaths and > 800,000 disability-adjusted life years(DALYs) in the European Economic Area [2]. By 2050,AMR is expected to cause > 1.3 million deaths in Europeand > 10 million deaths in the world annually [3]; low in-come countries and communities with health disparitiesare most affected [1, 4]. Recognition of the economicand health consequences of AMR has sparked globalefforts to reduce the overuse of antibiotics.Global commitment and multi-sector partnerships are

critical to implement effective interventions to reduceunnecessary use of antibiotics and control AMR [5, 6].Global survey data suggest that most antibiotics are usedin non-hospitalized populations [5]. In the UnitedKingdom (UK) from 2013 to 2017, only 20% of antibioticswere prescribed in hospitals, whereas 80% were prescribedin outpatient settings, with no clinical justification for atleast 30% [7, 8]. Despite this, in-hospital interventionshave predominated and preventive interventions inthe general public or community have been over-looked [9–11].The World Health Organization’s (WHO) global

action plan on AMR has five objectives, the first ofwhich is to improve knowledge and understanding ofAMR through communication, education and training[12]. National initiatives have adopted this plan, includ-ing the UK’s 20-year vision to combat AMR by 2040.The UK’s vision has nine goals, one of which is to makesure local communities understand all aspects of AMR[13]. Underscoring both WHO and UK missions, is thatknowledge promotes appropriate antibiotic use andreduces antibiotic overuse and AMR. However, theassociation between knowledge and antibiotic use in thegeneral population remains unclear [14].One model targeting behavioural change related to

AMR suggests that knowledge, environmental change,social/professional roles, social influences, and public be-liefs and priorities, among others, are important factors.Thus, multipronged public health interventions are ne-cessary to address AMR and to foster behaviour change[15]. Qualitative studies indicate that the public is un-aware of their role and responsibility in combattingAMR [16]. The public needs to be engaged via simple,awareness-raising messages [17]. At the same time, pub-lished quantitative data are conflicting. For example,some studies report that people with low knowledgeabout antibiotics are more likely to use antibiotics [18].Others report that people with more knowledge aboutantibiotics are more likely to use antibiotics [19, 20]; andothers show no association [21, 22]. To inform public

health interventions, it remains critical to crystallize howknowledge is associated with the likelihood of antibioticuse at the population level.Given this backdrop, we took advantage of a big data

resource – the Health Survey Northern Ireland 2014/15(HSNI14/15) – to examine the association betweenknowledge regarding infections and AMR and antibioticuse. Since multi-prong interventions are necessary to de-crease AMR, HSNI data allowed us to consider sex, agegroup, sociodemographic and behavioural factors,deprivation, and self-rated general health and satisfactionwith life. These factors have been previously associatedwith knowledge and antibiotic use [23–27]. Given theconflicting evidence base about the temporality anddirection of the relationship between knowledge andbehaviour, our non-directional hypothesis was that theexposure, knowledge of treatment of common infectiousdiseases and AMR would be associated with the out-come, self-reported antibiotic use in the past 12 months.

MethodsParticipants: health survey Northern Ireland 2014/15The HSNI is a series of independent, cross-sectional sur-veys conducted annually since 2010 by Northern IrelandStatistics Research on behalf of the Northern IrelandDepartment of Health, Social Services and Public Safety.The HSNI examines a wide range of public health issuesthat are important for the people of Northern Ireland. Inthis analysis, we utilised data from HSNI14/15, con-ducted between April 1, 2014 and March 31, 2015.HSNI14/15 consisted of a random sample of 5850 streetaddresses that were representative of the general popula-tion in Northern Ireland [28]. A vacant address was noteligible for inclusion.Eligible households were sent a letter of invitation via

post. The letter stated that the survey was being con-ducted on behalf of the Department of Health, SocialServices and Public Safety, Central Survey Unit, which isthe leading social survey research organisation inNorthern Ireland. The letter also stated that an officialinterviewer would visit the household to explain moreabout the study and that the survey could be resched-uled if necessary. Non-response occurred when aneligible address was not reached by an interviewer orwhen respondents refused to participate. To minimisenon-response, an interviewer attempted up to four callsto each eligible household unless the respondents hadpreviously refused to participate.HSNI14/15 surveyed knowledge of infections, antibi-

otics and antibiotic resistance, and use of antibioticsover the past 12 months. Additionally, the surveycollected sociodemographic, behavioural (smoking andalcohol drinking habits), and general health factorsinformation [28].

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Ethical approvalThe study protocol of HSNI14/15 was approved byNorthern Ireland Department of Health. Informedconsent was provided when the interviewer visited thehousehold. Participants were able to refuse to participateat any stage of the survey [29]. No incentive wasprovided for participation. Data analyses presented inthis paper were solely a secondary analysis of publiclyavailable HSNI data. Administrative permission was ne-cessary to access and use these HSNI data. Administra-tors of HSNI granted this access, provided the data file,and approved their use to the first author, JS.

Data collectionSurvey data from HSNI14/15 were collected usingComputer-Assisted Personal Interviewing (CAPI). CAPIis a face-to-face interviewing method. HSNI14/15 inter-viewers visited the respondents with a computer, read thequestions to the respondents, and entered the responsesimmediately on the computer during the course of theinterview. If a participant did not want to engage in aCAPI interview, they were offered a self-administered,closed booklet version of the HSNI14/15 that containedthe questions and response options. Responses weremanually entered later [28].

Study variablesOur primary exposure and outcome variables were locatedin the ‘Medication’ section of the HSNI14/15. Within thissection, there was a subsection dedicated to antibiotics.This section was comprised of seven questions.

Primary outcome variable: antibiotic useThe answer to the first question in the ‘Medications’ sec-tion, subsection ‘Antibiotics’, was the primary outcomeused in our analysis. The first question was ‘In the past12 months, have you taken an antibiotic?’ Response op-tions included: ‘Yes’, ‘No’ or ‘Don’t know’ [28].

Primary exposure variables: knowledge about infections,antibiotics and antibiotic resistanceThe following six questions in the section on ‘Medica-tions’, subsection ‘Antibiotics’, were used as the primaryexposure. These six questions assessed knowledge aboutinfections, antibiotics and antibiotic resistance (Table 1).Each question had the response options: ‘true’, ‘false’, or‘don’t know’. The six statements and correct responseswere as follows: 1) Colds and flus should be treated withantibiotics (false). 2) Antibiotics are used to treat bacter-ial infections (true). 3) Once you start to feel better, youshould stop taking the antibiotic (false). 4) You increaseyour chances of developing drug-resistant bacteria if youtake antibiotics when you don’t need to (true). 5) Youincrease your chances of developing drug-resistantbacteria if you do not finish the course of antibiotics(true). 6) Diseases such as tuberculosis, pneumoniaand meningitis are becoming more difficult to treat,as drug-resistant bacteria do not respond to antibi-otics and continue to cause infection (true) [28].To examine how knowledge about infections, antibi-

otics and antibiotic resistance was associated with anti-biotic use in the past 12 months, the knowledge variablewas considered as a correct answer to each individualstatement (0 = incorrect; 1 = correct), as well as a com-posite knowledge score. The knowledge score was thesum of answers to all six statements. The score rangewas 0–6, where a score of 0 corresponded to incorrectanswers on all 6 statements and a score of 6 was a cor-rect answer to all statements.

Potential covariates and confoundersWe selected potential covariates and confounders basedon published associations with knowledge, behaviour,and awareness and antibiotic use [23], as well as avail-ability in the HSNI data set.Demographic characteristics: included sex (Male/

Female) and age group (16–24, 25–44, 45–64,and > 65 years) [24].

Table 1 Number and proportion of participants correctly responding to statements regarding knowledge about infections,antibiotics and antibiotic resistance

Knowledge about infections, antibiotics, and antibiotic resistance

Correct response Correct responseN (% of 4135)

1 Colds and flus should be treated with an antibiotic False 3805 (92.0)

2 Antibiotics are used to treat bacteria True 2687 (65.0)

3 Once you start to feel better, you should stop taking the antibiotic False 3828 (92.6)

4 Chances of resistant bacteria increase if you take antibiotics when you don’t need to. True 2297 (55.6)

5 Chances of resistant bacteria increase if you don’t finish the course. True 1809 (43.7)

6 Diseases such as tuberculosis, pneumonia and meningitis are becoming more difficultto treat, as drug-resistant bacteria do not respond to antibiotics and continue tocause infection

True 2072 (50.1)

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Health behaviours: included current cigarette smokingand alcohol drinking [27]. Current cigarette smokingwas an answer of ‘yes’ to the question, ‘Do you smokecigarette at all nowadays?’. Current alcohol drinking wasan answer of ‘yes’ to the question, ‘Do you ever drinkalcohol nowadays, including drinks you brew or make athome?’Sociodemographic factors: included education attained

and income [25]. A deprivation index derived from theNorthern Ireland Multiple Deprivation Measure 2010(NIMDM) [30] was used to estimate socioeconomic andsociodemographic disparities. The NIMDM combinesgeographical rankings of seven domains of deprivationweighted as follows: income (25%), employment (25%),health deprivation and disability (15%), education, skillsand training (15%), proximity to services (10%), livingenvironment (5%), and crime and disorder (5%). Quintilesof NIMDM rankings based on a participant’s homeaddress comprise the deprivation index. Quintile 1 consti-tutes the most deprived whereas quintile 5 is the leastdeprived.General health status: Self-rated general health and life

satisfaction were components of the HSNI [26]. Self-rated general health over the past twelve months wasdescribed as ‘good’, ‘fairly good’ and ‘not good’. Life sat-isfaction was rated as ‘very satisfied’, ‘satisfied’, ‘neithersatisfied nor dissatisfied’, ‘dissatisfied’, and ‘very dissatis-fied’. We transformed the five life satisfaction categoriesinto three: satisfied (including very satisfied and satis-fied), neither satisfied nor dissatisfied, and dissatisfied(including dissatisfied and very dissatisfied).

Statistical analysesParticipant characteristics and knowledge about infec-tions and antibiotic resistance were described usingfrequencies and percentages. The Pearson’s chi-squaredtest was used to test for associations between categoricalvariables.To examine the association between the exposure,

knowledge about infections and antibiotic resistance andthe outcome, self-reported antibiotic use over the past12 months, weighted logistic regression analyses wereused with ‘no reported use’ as the referent. Samplingweights were used to estimate robust standard errors inboth unadjusted and adjusted models. The HSNI14/15sample was weighted by sex and age using the NorthernIreland 2014 mid-year population estimates. Odds ratios(OR) and 95% confidence intervals (CI) were estimated.The primary exposures were: 1) a correct answer to

each of six knowledge statements, and 2) a compositeknowledge score derived as the sum of correct answersto the six knowledge statements about infections andantibiotic resistance. Knowledge scores range from 0 to6 with a score of 0 representing 0 correct answers to all

6 statements, and a score of 6 representing 6 correct an-swers to all 6 statements. First, we ran six models esti-mating whether the correct answer to each individualstatement was associated with antibiotic use in the past12 months. Second, using the sum of correct answers tothe six statements regarding knowledge about infectionsand antibiotic resistance, we estimated whether know-ledge score (maximum score 6/6 correct answers) wasassociated with antibiotic use in the past 12 monthswhen compared to a lower knowledge score (score < 6/6correct answers).Univariate logistic regression analyses were followed

by multivariable analyses. Sex, age group, smoking andalcohol drinking, deprivation index, self-rated generalhealth, and satisfaction with life, were evaluated a priorias potential covariates in the multivariable models.Potential covariates with a p < 0.10 in univariate logisticanalyses were included in multivariable models. Multi-variable models were later stratified by sex and agegroup (removing sex and age as covariates, respectively).Results were considered statistically significant atp < 0.05 using two-sided tests. All statistical analyseswere performed using Stata 16.0.

ResultsOf 5850 randomly selected street addresses, 981 were in-eligible since the address was vacant, leaving an effectivesample of 4869 households. Among these households,28.6% (1394 of 4869 households) refused to participateand 8.1% (392 of 4869 households) were not reached.Thus, 63.3% (3083 of 4869) of households completed thesurvey, with some households providing data from morethan one resident. As a result, HSNI14/15 data providedby the UK Data Service included 4207 individuals aged16 years and older (1.36 individuals per household). Ofthese 4207 participants, 1.8% (n = 72) had missing datafor questions comprising knowledge about infectionsand antibiotic resistance, self-reported antibiotic use,current cigarette smoking status, drinking status, self-rated general health, satisfaction with life or sampleweight, and were excluded. This left 4135 participantsincluded in our analysis. The majority of these HSNI14/15 participants were women (58.9%), older than 25 years(94.0%), current non-smokers (79.2%), and current alco-hol drinkers (75.3%). There was a relatively equal distri-bution of participants across deprivation quintiles.Among all 4135 participants, use of an antibiotic in

the past 12 months was self-reported among 39.0% (n =1614). Women were more likely to have used anantibiotic compared to men (64.7% vs 35.3%, p < 0.001).Participants who provided correct answers to the sixstatements regarding knowledge about infections andantibiotic resistance, which comprised our primary ex-posure, are presented in Table 1. Eight percent (n = 334)

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of participants responded ‘don’t know’ to all sixquestions.HSNI14/15 participant characteristics by self-reported

antibiotic use in the past 12 months are presented inTable 2. Notably self-reported antibiotic use was associ-ated with all characteristics we considered includingknowledge score, sex, age group, current smoking status,deprivation index, general health and satisfaction withlife (p’s < 0.10), except alcohol drinking (p = 0.321).

Therefore, alcohol drinking was not included inmultivariable-adjusted models.The association between knowledge of infections and

antibiotic resistance and antibiotic use was estimatedusing weighted odds of antibiotic use in the past 12months predicted by knowledge defined by a correct an-swer to each individual statement, as well as knowledgescore (Table 3). Evaluation of individual statementsshowed a 61% higher odds (multivariable-adjusted OR

Table 2 Characteristics of 4135 HSNI14/15 participants by self-reported antibiotic use in the past 12 months

Characteristics Use of antibiotics in the past 12months

NoN = 2521n (%)

YesN = 1614n (%)

Chi-square† (df) P†

Knowledge about infections and antibiotic resistance 31.62 (6) < 0.001

0 (lowest knowledge) 253 (10.0) 100 (6.2)

1 70 (2.8) 45 (2.8)

2 99 (3.9) 61 (3.8)

3 517 (20.5) 374 (23.2)

4 631 (25.0) 380 (23.5)

5 592 (23.5) 360 (22.3)

6 (highest knowledge) 359 (14.2) 294 (18.2)

Sex 36.44 (1) < 0.001

Male 1129 (44.8) 570 (35.3)

Female 1392 (55.2) 1044 (64.7)

Age group (years) 10.53 (3) 0.015

16–24 153 (6.1) 94 (5.9)

25–44 793 (31.4) 503 (31.1)

45–64 899 (35.6) 515 (31.9)

> 65 676 (26.9) 502 (31.1)

Current smoker, Yes 485 (19.3) 376 (23.3) 9.83 (1) 0.002

Alcohol drinker, Yes 1920 (76.2) 1193 (73.9) 2.66 (1) 0.102

Deprivation quintiles 29.34 (4) < 0.001

1 (most deprived) 362 (14.4) 306 (19.0)

2 490 (19.4) 343 (21.2)

3 536 (21.2) 363 (22.5)

4 592 (23.5) 325 (20.2)

5 (least deprived) 541 (21.5) 277 (17.1)

General health 209.55 (2) < 0.001

Good 1643 (65.2) 738 (45.7)

Fairly good 610 (24.2) 458 (28.4)

Not good 268 (10.6) 418 (25.9)

Satisfaction with life 33.47 (2) < 0.001

Satisfied 2258 (89.6) 1358 (84.1)

Neither satisfied nor dissatisfied 177 (7.0) 144 (8.9)

Dissatisfied 86 (3.4) 112 (7.0)

†: Two-sided Pearson chi-square statistics (degrees of freedom) and P valueHSNI: Health Survey Northern Ireland

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1.61, 95% CI [1.38,1.88], p < 0.001) of antibiotic use asso-ciated with a correct answer to the statement ‘Antibi-otics are used to treat bacteria’ and 19% higher odds(adjusted OR 1.19, 95% CI [1.03,1.37], p < 0.001) of anti-biotic use with a correct answer to the statement‘Chances of resistant bacteria increase if you don’t finishthe course’. A breakdown of knowledge score (the sumof correct answers to six statements about infections andantibiotic resistance (range 0–6)) by number correct an-swers showed that 8.5% (N = 353) of participants had 0/6correct answers, 2.8% (N = 115) had 1/6 correct answers,3.8% (N = 160) had 2/6 correct answers, 21.6% (N = 891)had 3/6 correct answers, 24.6% (N = 1011) had 4/6 cor-rect answers, 23.0% (N = 952) had 5/6 correct answersand 15.8% (N = 653) had 6/6 correct answers.Estimates of the odds of antibiotic use in the past 12

months associated with our primary exposure, know-ledge score and each characteristic, were accomplishedusing weighted multivariable-adjusted models and arepresented in Table 4. Higher knowledge score was asso-ciated with higher odds of antibiotic use (p for trend <0.001), with adjustment for sex, age, current smoking,deprivation quintiles, and self-rated general health andsatisfaction with life. However, knowledge was not theonly characteristic associated with higher odds of anti-biotic use. Women compared to men (adjusted OR 1.54,95% CI [1.32, 1.79], p < 0.001) and those reporting poorversus good general health (adjusted OR 3.53, 95% CI[2.84, 4.40], p < 0.001) were more likely to report usingan antibiotic in the past 12 months. Those aged 45–64

years compared to those age 16–24 years (adjusted OR0.71, 95% CI [0.52, 0.96], p = 0.024) and those compris-ing the 5th deprivation quintile (the least deprived) com-pared to the referent 3rd deprivation quintile (adjustedOR 0.73, 95% CI [0.58, 0.92], p = 0.007) were less likelyto report antibiotic use.Sex and age group stratification illustrated some nuances

to these data. Among both women and men, the highestknowledge score (6/6 vs. 0/6 correct answers) was associ-ated with 2 times higher odds of antibiotic use in the past12months; and poor self-rated health was linked to higherodds of antibiotic use. However, deprivation index was acharacteristic associated with antibiotic use among menonly, and when compared to the middle deprivationquintile, men comprising the least deprivation quintilesexhibited 42% lower odds of antibiotic use (SupplementalTable S1). Age group was a characteristic associated withantibiotic use among women only and women aged 45–64years reported lower odds of antibiotic use.Age group stratification (Supplemental Table S2) evi-

denced that the positive association between antibioticuse and knowledge was weakest among 16–24 year-olds.Being a woman was positively associated with antibioticuse among those ages 25–64 years; smoking amongthose age 45–64 years; and poorer than good self-ratedgeneral health among those age 25 years and older.

DiscussionData from the HSNI14/15 evidenced that higher know-ledge about antibiotic use in common infections and

Table 3 Odds of self-reported antibiotic use by correct answer to individual knowledge statements. HSNI14/15

Odds of self-reported antibiotic use in the past 12months

Model‡ Unadjusted Adjusted†

Statements regarding knowledge about infections andantibiotic resistance

OR (95% CI) P OR (95% CI) P

1 Colds and flus should be treated with an antibiotic Incorrect 1.00 (ref) 1.00 (ref)

Correct 1.15 (0.95–1.40) 0.156 1.01 (0.76–1.33) 0.967

2 Antibiotics are used to treat bacteria Incorrect 1.00 (ref) 1.00 (ref)

Correct 1.53 (1.32–1.78) < 0.001 1.61 (1.38–1.88) < 0.001

3 Once you start to feel better, you should stop taking the antibiotic Incorrect 1.00 (ref) 1.00 (ref)

Correct 1.33 (1.08–1.63) 0.007 1.15 (0.87–1.53) 0.335

4 Chances of resistant bacteria increase if you take antibiotics whenyou don’t need to.

Incorrect 1.00 (ref) 1.00 (ref)

Correct 0.99 (0.86–1.14) 0.909 1.03 (0.89–1.20) 0.653

5 Chances of resistant bacteria increase if you don’t finish the course. Incorrect 1.00 (ref) 1.00 (ref)

Correct 1.20 (1.04–1.38) 0.011 1.19 (1.03–1.37) 0.021

6 Diseases such as tuberculosis, pneumonia and meningitis arebecoming more difficult to treat, as drug-resistant bacteria do notrespond to antibiotics and continue to cause infection

Incorrect 1.00 (ref) 1.00 (ref)

Correct 0.95 (0.83–1.10) 0.514 1.03 (0.89–1.19) 0.728

‡ Model 1 to 6: Unadjusted and adjusted odds ratio (OR) and 95% confidence interval (95% CI) with Robust standards errors for use of antibiotics in the past 12months associated with correct answers to the six statements concerning infections and antibiotic resistance† Adjusted for sex, age group, cigarette smoking, deprivation quintiles, self-rated general health, and satisfaction with lifeHSHI: Health Survey Northern Ireland

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Table 4 Odds of self-reported antibiotic use by knowledge about infections and antibiotic resistance, and covariates. HSNI14/15

Odds of self-reported antibiotic use in the past 12months

Unadjusted Adjusted†

OR (95% CI) ‡ P OR (95% CI) ‡ Pa

Knowledge score (0–6)

0 (lowest knowledge) 1.00 (ref) 1.00 (ref)

1 1.20 (0.73–1.98) 0.476 1.15 (0.65–2.01) 0.633

2 1.24 (0.79–1.94) 0.358 1.19 (0.76–1.86) 0.452

3 1.57 (1.15–2.12) 0.004 1.60 (1.16–2.20) 0.004

4 1.29 (0.96–1.75) 0.094 1.35 (0.98–1.85) 0.064

5 1.37 (1.02–1.86) 0.039 1.46 (1.06–2.00) 0.020

6 (highest knowledge) 1.81 (1.32–2.47) < 0.001 2.03 (1.46–2.81) < 0.001

Sex

Male 1.00 (ref) 1.00 (ref)

Female 1.57 (1.36–1.82) < 0.001 1.54 (1.32–1.79) < 0.001

Age (years)

16–24 1.00 (ref) 1.00 (ref)

25–44 1.02 (0.77–1.37) 0.873 0.93 (0.69–1.25) 0.637

45–64 0.91 (0.69–1.22) 0.539 0.71 (0.52–0.96) 0.024

65+ 1.20 (0.90–1.61) 0.211 0.85 (0.63–1.16) 0.305

Current smoker

No 1.00 (ref) 1.00 (ref)

Yes 1.29 (1.09–1.53) 0.003 1.12 (0.94–1.35) 0.212

Alcohol drinker†

Non-drinker 1.00 (ref)

Drinker 0.92 (0.78–1.08) 0.314

Deprivation quintiles

1(most deprived) 1.14 (0.91–1.43) 0.254 1.01 (0.80–1.28) 0.922

2 0.95 (0.77–1.18) 0.656 0.94 (0.75–1.17) 0.591

3 1.00 (ref) 1.00 (ref)

4 0.73 (0.59–0.90) 0.003 0.76 (0.61–0.95) 0.014

5(least deprived) 0.70 (0.56–0.87) 0.001 0.73 (0.58–0.92) 0.007

Self-rated health

Good 1.00 (ref) 1.00 (ref)

Fairly 1.66 (1.41–1.96) < 0.001 1.71 (1.43–2.04) < 0.001

Not good 3.43 (2.84–4.14) < 0.001 3.53 (2.84–4.40) < 0.001

Satisfaction with life

Satisfied 1.00 (ref) 1.00 (ref)

Neither satisfied nor dissatisfied 1.35 (1.05–1.73) 0.018 0.81 (0.62–1.07) 0.132

Dissatisfied 2.26 (1.66–3.09) < 0.001 1.21 (0.82–1.78) 0.335

‡ Odds ratio (OR) and 95% confidence interval (95% CI) with Robust standards errors† Adjusted for sex, age, cigarette smoking, deprivation quintiles, self-rated general health, and satisfaction with life based on P values < 0.250 inunivariate analysesa: P value for trend of knowledge score was < 0.001HSNI: The Health Survey of Northern Ireland

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antibiotic resistance was associated with highermultivariable-adjusted odds of self-reported antibioticuse in the past 12 months. This was especially observedamong women, those 25 years or older, and those withpoorer self-rated general health. Those who were leastdeprived were less likely to report antibiotic use in thepast 12 months.Our results support previous reports that people who

are more knowledgeable about antibiotics and antibioticresistance are more likely to use antibiotics than peoplewho are less knowledgeable [19, 20]. Another studyconducted in the United Kingdom, found that adultswith good knowledge about antibiotics had better atti-tudes towards antibiotic use, but not lower antibioticconsumption [21]. A higher likelihood of antibiotic useamong people with high knowledge about infections andantibiotic resistance suggests that this knowledge may beinsufficient to curb higher use of antibiotics [31].Contrastingly, antibiotic use may also lead to betterknowledge about antibiotics.Knowledge is only one component of health literacy,

which is ‘the degree to which individuals have thecapacity to obtain, process, and understand basic healthinformation and services needed to make appropriatehealth decisions’ [32]. Health literacy influences thecomplete spectrum of health behaviour and healthbehaviour change to offset unhealthy behaviour [32].Therefore, knowledge may be insufficient to influenceantibiotic overuse. Good, multi-faceted health literacy isrequired to make the critical decisions necessary to re-duce antibiotic overuse [33]. As reported previously,people with high knowledge about antibiotics reportedhigher antibiotic use, whereas people with good healthliteracy used an antibiotic significantly less frequentlycompared with people with insufficient health literacy[19]. Our finding of a positive association betweenknowledge and antibiotic use supports this finding.Those who use antibiotics may gain knowledge fromprescribers, pharmacists, or other sources of health in-formation such as the internet [21].Since most published studies are cross-sectional, the

causal relationship between knowledge and antibioticuse cannot be determined. Well-delivered knowledge isempowering and a prerequisite of health literacy [34],and informed choice is not possible unless people havethe relevant knowledge. Experiences from the UK’s Anti-biotic Guardian campaign suggest awareness-raisingmessages focused to people with poor antibiotic resist-ance knowledge are likely to be effective [17]. Still,HSNI14/15 data cannot be temporally elucidated.Among HSNI14/15 participants, men and women did

not differ on knowledge about infections and antibioticresistance; however, women were more likely to reportantibiotic use than men. Sex differences in health-seeking

behaviour are observed in other Western societies wherewomen seek healthcare more frequently than men [35].Whether this is related to knowledge is unclear. Severalstudies show that women have more frequent contactwith healthcare providers over their life course, comparedto men. This may be almost solely due to female repro-ductive factors such as pregnancy, birthing, and the meno-pause, as well as experience with antibiotics while caringfor children and other family members [36–38]. Multiplecontacts with healthcare providers over time are great op-portunities for health education to reduce undesiredhabits including antibiotic overuse. However, due to clin-ical time constraints and bottom lines related to clinicalrevenues, physicians tend to prescribe antibiotics moreoften than they inform on antibiotic overuse and the dan-ger of antibiotic resistance [31].Compared to other age groups, younger (16–24 years)

HSNI14/15 participants comprised the largest proportion(92%, 228/247) with lesser knowledge about infectionsand antibiotic resistance, as has been previously reported[24, 39]. Younger people have had fewer opportunities tointeract with the healthcare system, and are less likely tohave assumed responsibility for child or aging parent care.Thus, they may lack experience with antibiotics, and con-sequently have less knowledge about health issues includ-ing infectious diseases and antibiotics [24].In HSNI14/15, there is some evidence of a gradient of

association between deprivation and antibiotic use.Compared with the 3rd deprivation quintile, the odds ofantibiotic use in the past 12 months were not differentamong the most deprived (3rd and 2nd deprivationquintiles), but were lower among the least deprived (4thand 5th deprivation quintiles). NIMDM deprivationindex is a composite of income, employment, healthdeprivation and disability, education, skills and training,proximity to services, living environment, and crime anddisorder, with consideration for home address. Manystudies have shown that individuals comprising lowersocioeconomic classes tend to be less knowledgeableabout infections and antibiotic resistance compared withindividuals in higher socioeconomic classes [40]. Whilewe cannot address which component of the NIMDMdeprivation index drives the observed association withantibiotic use, this index is weighted most heavily for in-come (25%) and employment (25%). Our data concur,such that lesser deprivation was associated with lowerodds of antibiotic use. It has also been shown that indi-viduals experiencing the most deprivation are morelikely to have multiple chronic diseases and a greater riskfor infectious diseases. This has been markedly evidentduring the corona virus disease 2019 (COVID-19) pan-demic [41].Poorer self-rated general health was also associated

with higher odds of antibiotic use in HSNI14/15. This

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may have been observed for several reasons, including:1) co-occurring physical disease or co-morbidities, someof which may increase the risk of a bacterial infection[42]; 2) psychological distress that may adversely influ-ence coping with common infections [43]; and 3) ahigher likelihood of reporting clinical illness with infec-tion for which physicians are prescribing antibioticsmore readily [44].

Strengths and limitationsOur analysis has several strengths. The HSNI14/15includes a representative, population-based sample ofmore than 4000 participants, across a wide age range.Self-reported data were collected using well-validatedscales and state-of-the-art CAPI methods withoutreliance on population-based prescribing statistics; andthe calculated deprivation index is comprehensive andinformative. In addition, multivariable analyses allowevaluation of the association between knowledge andantibiotic use after controlling for potentially relevantconfounders, lending to the robustness of our result.Given the relatively few studies in this field, these dataare highly valuable and fill a gap in the literature.Despite these strengths, as with many large-scalepopulation-based surveys, there are limitations. First, theHSNI uses a serial cross-sectional design comprised ofindependent samples. Despite the agreement of our re-sults with other published studies, we cannot determinecausality, i.e., whether knowledge influences behaviour(antibiotic use) or vice versa. Second, HSNI data areself-reported, thus, there is inherent recall bias. Regard-ing the face-to-face efficiency of CAPI, underreporting ismore frequent in face-to-face interviewing of sensitive,stigmatized or unpopular behaviours [45]. This risk wasreduced in HSNI14/15 because alternatives, such asComputer-Assisted Self-Interview (CASI) or pen-and-paper, were available. Third, as the HSNI is geographicallynarrow in scope, our findings may not be generalizable toother regions of Europe, other ‘Western’ countries, or glo-bally. However, even in Low- or Middle-Income countries,socioeconomics, sex and gender expectations, antibioticprescribing practices, self-medication, and medicineregulations, are related to knowledge and antibiotic use ina similar fashion. For example, and as mentioned, individ-uals from lower socioeconomic classes tend to be lessknowledgeable about infections and antibiotic resistancecompared to those in higher socioeconomic classes [40],which is mimicked by our observation using the NIMDMdeprivation index. Furthermore, sex differences in theassociation between knowledge about infections and anti-biotic resistance and antibiotic use are universal, regard-less of country or context [18, 39]. Fourth, we are unableto address response bias using this dataset. Fifth, as withany large-scale survey, there is a limit to the number of

questions that can be asked. For example, in the HSNI,information is lacking on self-medication via sharing ofprescription antibiotics [46], the role of different health-care providers in facilitating accrual of knowledge [47],and the important contribution of responsible healthcareproviders’ prescribing practices. Finally, given the need formulti-pronged interventions specific to reducing AMR,surveys providing the basis for AMR interventionsneed to provide the distinct data necessary to accom-plish that goal [15].

ConclusionHSNI14/15 data illustrate that there was a positivecross-sectional association between knowledge aboutinfections and antibiotic resistance and antibiotic use.However, we also observed a number of common modi-fiable and non-modifiable risk factors associated withboth self-reported antibiotic use and basic knowledge oftreatment of common infectious diseases and antibioticresistance. These factors represent potential ‘at risk’groups and thus, allow the creation of targeted cam-paigns that are critical for increasing knowledge ingeneral, supporting informed choices, and raising thepublic’s health literacy. In addition, future researchshould prospectively examine whether knowledge aboutinfections, antibiotics and antibiotic resistance predictsquantity or frequency of antibiotic use; as well as identifyoptimal types of information and methods of informa-tion dissemination that reduce antibiotic overuse. It is ofcritical importance that future prospective studies in-clude measures of knowledge and attitudes, as well asdedicated measures of health literacy.

AbbreviationsAMR: Antimicrobial resistance; CAPI: Computer-Assisted Personal Interview;CASI: Computer-Assisted Self-Interview; CI: Confidence interval; COVID-19: Corona virus disease 2019; HSNI: Health Survey Northern Ireland; NIMDM: Northern Ireland Multiple Deprivation Measure; OR: Odds ratio; UK: UnitedKingdom; USA: United States of America; WHO: World Health Organization

Supplementary InformationThe online version contains supplementary material available at https://doi.org/10.1186/s12889-021-11018-x.

Additional file 1.

Additional file 2.

AcknowledgementsWe acknowledge program support from the University of Skövde, Sweden,and the State University of New York, USA.

Authors’ contributionsStudy conception and design: JS, EO. Data acquisition: JS. Data analysis: JS,DG. Data interpretation: JS, EO, KC, DG. Manuscript drafting and criticalreviewing: JS, EO, KC, DG. All the authors read and approved the finalmanuscript.

Shebehe et al. BMC Public Health (2021) 21:1041 Page 9 of 11

FundingThe authors received no specific funding for this work. Open Access fundingprovided by Örebro University.

Availability of data and materialsThe datasets analysed during the current study are available in the UK DataService repository, https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=8347.

Declarations

Ethics approval and consent to participateThe study protocol was approved by Northern Ireland Department of Health.All participants provided informed consent [29]. Data analyses presented inthis paper were solely a secondary analysis of publicly available HSNI data.Administrative permission was necessary to access and use these HSNI data.Administrators of HSNI granted this access, provided the data file, andapproved their use to the first author, JS.

Consent for publicationNot applicable.

Competing interestsThe authors declare that they have no competing interests.

Author details1Clinical Epidemiology and Biostatistics, School of Medical Sciences, ÖrebroUniversity, Örebro, Sweden. 2School of Health Sciences, University of Skövde,Skövde, Sweden. 3Department of Neurology, State University of New YorkDownstate Health Sciences University, Brooklyn, NY, USA.

Received: 7 September 2020 Accepted: 9 May 2021

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